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Found 14 papers, 4 papers with code
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration
The narrow-space search of floating-point models improves the accuracy on CIFAR10 of an established IoT model from 70. 64% to 74. 87% within the same memory constraints.
Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy
We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.
Constrained deep neural network architecture search for IoT devices accounting hardware calibration
We further improve the accuracy to 82. 07% by including 16-bit half types and we obtain the best accuracy of 83. 45% by extending the search with model optimized IEEE 754 reduced types.
PAGAN: Portfolio Analysis with Generative Adversarial Networks
Sound financial theories demonstrate that in an efficient marketplace all information available today, including expectations on future events, are represented in today prices whereas future price trend is driven by the uncertainty.
Computational Finance Statistical Finance
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion.
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Organic synthesis is one of the key stumbling blocks in medicinal chemistry.
Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale
In this paper, we present a modular, cloud-based platform to ingest documents at scale.
Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract]
We present a platform to ingest documents at scale which is powered by Machine Learning techniques and allows the user to train custom models on document collections.
Incremental Training of Deep Convolutional Neural Networks
We propose an incremental training method that partitions the original network into sub-networks, which are then gradually incorporated in the running network during the training process.
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
In the deep-learning community new algorithms are published at an incredible pace.
BAGAN: Data Augmentation with Balancing GAN
The generator in the GAN is initialized with the encoder module of an autoencoder that enables us to learn an accurate class-conditioning in the latent space.
"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models
With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy.
Mixed-Precision In-Memory Computing
As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to significantly extend the performance of today's computers.
Emerging Technologies
